• 제목/요약/키워드: Computational Mechanics

검색결과 741건 처리시간 0.022초

The stick-slip decomposition method for modeling large-deformation Coulomb frictional contact

  • Amaireh, Layla. K.;Haikal, Ghadir
    • Coupled systems mechanics
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    • 제7권5호
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    • pp.583-610
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    • 2018

  • This paper discusses the issues associated with modeling frictional contact between solid bodies undergoing large deformations. The most common model for friction on contact interfaces in solid mechanics is the Coulomb friction model, in which two distinct responses are possible: stick and slip. Handling the transition between these two phases computationally has been a source of algorithmic instability, lack of convergence and non-unique solutions, particularly in the presence of large deformations. Most computational models for frictional contact have used penalty or updated Lagrangian approaches to enforce frictional contact conditions. These two approaches, however, present some computational challenges due to conditioning issues in penalty-type implementations and the iterative nature of the updated Lagrangian formulation, which, particularly in large simulations, may lead to relatively slow convergence. Alternatively, a plasticity-inspired implementation of frictional contact has been shown to handle the stick-slip conditions in a local, algorithmically efficient manner that substantially reduces computational cost and successfully avoids the issues of instability and lack of convergence often reported with other methods (Laursen and Simo 1993). The formulation of this approach, however, has been limited to the small deformations realm, a fact that severely limited its application to contact problems where large deformations are expected. In this paper, we present an algorithmically consistent formulation of this method that preserves its key advantages, while extending its application to the realm of large-deformation contact problems. We show that the method produces results similar to the augmented Lagrangian formulation at a reduced computational cost.

  • https://doi.org/10.12989/csm.2018.7.5.583 인용 KSCI

공작기계 LM 베어링의 정동적 특성을 반영하는 전산 모델링 (A Computational Modeling Reflecting Static and Dynamic Characteristics of LM Bearings for Machine Tools)

  • 김혜연;정종규;원종진;정재일
    • 한국정밀공학회지
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    • 제29권10호
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    • pp.1062-1069
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    • 2012

  • This paper suggests a computational modeling to reflect static/dynamic characteristics of LM bearings. A theoretical study for modeling LM bearings is elucidated by using the Hertz contact theory, the Lagrange's equation of motion, normal mode analysis and a calculation of equivalent moment center. The complex geometry of LM bearings is replaced by a simplified model with eight springs only. The suggested model reflects static and dynamic characteristics of LM bearings without any consideration for the shape of the bed or stages on the LM bearings. The modal experimental results are compared to the simulation results with the suggested computational modeling. The difference between the experiments and simulation is calculated less than 8%.

  • https://doi.org/10.7736/KSPE.2012.29.10.1062 인용 PDF KSCI

Reliability-based combined high and low cycle fatigue analysis of turbine blade using adaptive least squares support vector machines

  • Ma, Juan;Yue, Peng;Du, Wenyi;Dai, Changping;Wriggers, Peter
    • Structural Engineering and Mechanics
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    • 제83권3호
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    • pp.293-304
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    • 2022

  • In this work, a novel reliability approach for combined high and low cycle fatigue (CCF) estimation is developed by combining active learning strategy with least squares support vector machines (LS-SVM) (named as ALS-SVM) surrogate model to address the multi-resources uncertainties, including working loads, material properties and model itself. Initially, a new active learner function combining LS-SVM approach with Monte Carlo simulation (MCS) is presented to improve computational efficiency with fewer calls to the performance function. To consider the uncertainty of surrogate model at candidate sample points, the learning function employs k-fold cross validation method and introduces the predicted variance to sequentially select sampling. Following that, low cycle fatigue (LCF) loads and high cycle fatigue (HCF) loads are firstly estimated based on the training samples extracted from finite element (FE) simulations, and their simulated responses together with the sample points of model parameters in Coffin-Manson formula are selected as the MC samples to establish ALS-SVM model. In this analysis, the MC samples are substituted to predict the CCF reliability of turbine blades by using the built ALS-SVM model. Through the comparison of the two approaches, it is indicated that the reliability model by linear cumulative damage rule provides a non-conservative result compared with that by the proposed one. In addition, the results demonstrate that ALS-SVM is an effective analysis method holding high computational efficiency with small training samples to gain accurate fatigue reliability.

  • https://doi.org/10.12989/sem.2022.83.3.293 인용 KSCI